一种变分潜在平衡用于神经回路中的学习
A Variational Latent Equilibrium for Learning in Neuronal Circuits
AI总结：
本文提出了一种变分潜在平衡方法，用于近似BPTT算法，统一并扩展了多种时空信用分配方法，为大脑时空深度学习提供了理论框架，并提出了物理电路的实现蓝图。
摘要：
大脑在识别和生成复杂时空模式的能力仍然无可匹敌。尽管人工智能能够复现其中的一些能力，但深度学习算法在当前对大脑回路和动态的理解上仍存在很大差距，尤其在反向传播时间（BPTT）算法上，这是学习复杂时间依赖性的主流方法。在本工作中，我们提出了一种通用的公式来以受控且生物合理的方式近似BPTT。我们的方法基于、统一并扩展了几种先前针对局部、时间连续、无相位的时空信用分配的方法，这些方法基于能量守恒和极值作用的原则。我们的起点是一个面向未来的神经元状态能量函数，从中计算出时间连续神经网络的实时误差动态。在一般情况下，这为神经网络提供了简单直接的推导方法，得出邻接方法的结果，这是BPTT的时间连续等价物。通过少量修改，我们可以将其转化为完全局部（在空间和时间上）的神经元和突触动态方程组。我们的理论为大脑的时空深度学习提供了一个严谨的框架，同时提出了能够执行这些计算的物理电路蓝图。这些结果重构并扩展了最近提出的广义潜在平衡（GLE）模型。
英文摘要：
Brains remain unrivaled in their ability to recognize and generate complex spatiotemporal patterns. While AI is able to reproduce some of these capabilities, deep learning algorithms remain largely at odds with our current understanding of brain circuitry and dynamics. This is prominently the case for backpropagation through time (BPTT), the go-to algorithm for learning complex temporal dependencies. In this work we propose a general formalism to approximate BPTT in a controlled, biologically plausible manner. Our approach builds on, unifies and extends several previous approaches to local, time-continuous, phase-free spatiotemporal credit assignment based on principles of energy conservation and extremal action. Our starting point is a prospective energy function of neuronal states, from which we calculate real-time error dynamics for time-continuous neuronal networks. In the general case, this provides a simple and straightforward derivation of the adjoint method result for neuronal networks, the time-continuous equivalent to BPTT. With a few modifications, we can turn this into a fully local (in space and time) set of equations for neuron and synapse dynamics. Our theory provides a rigorous framework for spatiotemporal deep learning in the brain, while simultaneously suggesting a blueprint for physical circuits capable of carrying out these computations. These results reframe and extend the recently proposed Generalized Latent Equilibrium (GLE) model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
